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Galter, I. ; Schneltzer, E. ; Marr, C. ; Spielmann, N. ; Hrabě de Angelis, M.

EchoVisuAL: Efficient Segmentation of Echocardiograms Using Deep Active Learning.

In: (Medical Image Understanding and Analysis). Berlin [u.a.]: Springer, 2024. 366-381 (Lect. Notes Comput. Sc. ; 14860 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Echocardiography is a fast and cost-effective imaging technique for assessing cardiac function and structure. However, image-derived phenotypic evaluation is challenging. Current AI-approaches designed for automatic interpretation of echocardiography data are progressing, but algorithms for animal models frequently used in pre-clinical studies are rare. Here, we propose a deep active learning approach, called EchoVisuAL, that uses large-scale, multi-center data of the International Mouse Phenotyping Consortium (IMPC). This heterogeneous IMPC data set includes 96 392 echocardiograms with 3 831 290 frames from 17 991 mice. Heterogeneity is characterized by differences in age, sex, background strains, anesthesia, imaging frequency and focus depth. EchoVisuAL is founded on a Bayesian U-Net that produces inner trace segmentations alongside with two confidence metrics, an uncertainty measure and a BALD score. This architecture, embedded in an active learning framework, enables a substantial reduction of the annotation efforts by an intelligent selection of the next frames that should be annotated. In total, 15 models were trained on step-wise increasing training data sets based on the model’s confidence. For model evaluation, 25 echocardiograms with 1062 frames were annotated by four highly experienced, independent experts. Inter-rater-agreement across all frames was high with a mean Randolph’s kappa score of 0.91±0.10. Across models, high Dice scores were observed on these expert annotations, currently considered as the gold standard, with model M15 achieving a mean Dice score of 0.98±0.02. EchoVisuAL is a new deep active learning application robust to automatically analyze heterogeneous mouse echocardiograms, including uncertainty scores for user guidance.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Bayesian Neural Networks ; Cardiac Segmentation ; Deep Active Learning ; Echocardiography
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Medical Image Understanding and Analysis
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14860 LNCS, Issue: , Pages: 366-381 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute of Experimental Genetics (IEG)
Institute of AI for Health (AIH)
Grants Projekt DEAL
German Center for Diabetes Research (DZD)
German Federal Ministry of Education and Research
Hightech Agenda Bayern
ERC under the European Union's Horizon 2020 research and innovation program